powclasspred              package:gaga              R Documentation

_E_x_p_e_c_t_e_d _p_r_o_b_a_b_i_l_i_t_y _t_h_a_t _a _f_u_t_u_r_e _s_a_m_p_l_e _i_s _c_o_r_r_e_c_t_l_y _c_l_a_s_s_i_f_i_e_d.

_D_e_s_c_r_i_p_t_i_o_n:

     Estimates posterior expected probability that a future sample is
     correctly classified when performing class prediction. The
     estimate is obtained via Monte Carlo simulation from the posterior
     predictive.

_U_s_a_g_e:

     powclasspred(gg.fit, x, groups, prgroups, v0thre=1, ngene=100, B=100)

_A_r_g_u_m_e_n_t_s:

  gg.fit: GaGa or MiGaGa fit (object of type 'gagafit', as returned by
          'fitGG'). 

       x: 'ExpressionSet', 'exprSet', data frame or matrix containing
          the gene expression measurements used to fit the model.

  groups: If 'x' is of type 'ExpressionSet' or 'exprSet', 'groups'
          should be the name of the column in 'pData(x)' with the
          groups that one wishes to compare. If 'x' is a matrix or a
          data frame, 'groups' should be a vector indicating to which
          group each column in x corresponds to.

prgroups: Vector specifying prior probabilities for each group.
          Defaults to equally probable groups.

  v0thre: Only genes with posterior probability of being equally
          expressed below 'v0thre' are used.

   ngene: Number of genes to use to build the classifier. Genes with
          smaller probability of being equally expressed are selected
          first.

       B: Number of Monte Carlo samples to be used.

_D_e_t_a_i_l_s:

     The routine simulates future samples (microarrays) from the
     posterior predictive distribution of a given group (e.g.
     control/cancer). Then it computes the posterior probability that
     the new sample belongs to each of the groups and classifies the
     sample into the group with highest probability. This process is
     repeated 'B' times, and the proportion of correctly classified
     samples is reported for each group. The standard error is obtained
     via the usual normal approximation (i.e. SD/B). The overall
     probability of correct classification is also provided (i.e. for
     all groups together), but using a more efficient variant of the
     algorithm. Instead of reporting the observed proportion of
     correctly classified samples, it reports the expected proportion
     of correctly classified samples (i.e. the average posterior
     probability of the class that the sample is assigned to).

_V_a_l_u_e:

     List with components: 

   ccall: Estimated expected probability of correctly classifying a
          future sample.

 seccall: Estimated standard error of 'ccall'.

 ccgroup: Vector with the estimated probability of correctly
          classifying a sample from each group.

 segroup: Estimated standard error of 'ccgroup'.

_A_u_t_h_o_r(_s):

     David Rossell

_R_e_f_e_r_e_n_c_e_s:

     Rossell D. GaGa: a simple and  flexible hierarchical model for
     microarray data analysis. <URL:
     http://rosselldavid.googlepages.com>.

_S_e_e _A_l_s_o:

     'classpred', 'fitGG', 'parest'

